Proximal survival analysis to handle dependent right censoring

Abstract Many epidemiological and clinical studies aim to analyse a time-to-event endpoint. A common complication is right censoring. In some cases, right censoring occurs when subjects are still surviving after the study terminates or move out of the study area. In such cases, right censoring is typically treated as independent or noninformative. This assumption can be further relaxed to conditional independent censoring by leveraging possibly time-varying covariate information, if available, and assuming censoring and failure time are independent within covariate strata. In yet other instances, events may be censored by other competing events like death and are associated with censoring possibly through prognoses. Realistically, measured covariates can rarely capture all such associations with absolute certainty. In cases of dependent censoring, covariate measurements are often, at best, proxies of underlying prognoses. In this article, we establish a nonparametric identification framework by formally admitting that conditional independent censoring may fail in practice and accounting for covariate measurements as imperfect proxies of underlying association. The framework suggests adaptive estimators, and we provide generic assumptions under which they are consistent, asymptotically normal, and doubly robust. We examine the finite-sample performance of our proposed estimators via a Monte Carlo simulation and apply them to the SEER-Medicare dataset.